Optimal Consensus Control for Switching Uncertain Multiagent Systems Using Model Reference Control and Reinforcement Learning

This paper addresses the optimal consensus problem in uncertain switching multiagent systems. The inherent uncertainty and time-varying structure of local tracking error system render conventional methods ineffective for deriving optimal control protocols. To overcome these challenges, we introduce a reference model for each agent and construct a modified augmented local tracking error (ALTE) system. This approach transforms the optimal consensus problem into two sub-problems: 1) model reference control (MRC) between agents and their reference models; 2) distributed optimal stabilization of the modified ALTE system. We propose a new control scheme that combines filtered tracking error with equivalent input disturbance method to achieve MRC. To realize distributed optimal stabilization of the modified ALTE, we introduce a deep deterministic policy gradient method based on value iteration. Through theoretical analysis, we demonstrate that the multiagent system achieves a near Nash equilibrium, which is further validated by numerical simulation.